OpenRaiser/Intern-Atlas huggingface dataset:
Legends of Nature and Editors-in-chief
Venue: arXiv cs.DL 2019
Year: 2019

This year marks the 150th celebration of Nature. However, the understanding of the way the army of unsung editors-in-chief has strengthened and enriched the integrity and quality of the journal under the umbrella of its original mission remains nominal rather than substantial. This paper scrutinizes the chief vehicle guided by Nature's doctrine with regard to the ways it has conflicted with the advancement of both science and social progress. We first recast quantitative spatiotemporal analysis on the diachronic discourse of Nature since its debut, which promises to articulate the unfolding chronological picture of Nature on a historical time scale, and pinpoint overdue corrective to the strongly-held but flawed notions on editors-in-chief of Nature. Our findings strongly indicate that the army of editors-in-chief have never met with their fair share of identification, and they took on the challenge guided by Nature's doctrine with extraordinary polymath, unparalleled enthusiasm and diverse characters.
Overview of Logical Foundations of Cyber-Physical Systems
Venue: arXiv cs.LO 2019
Year: 2019

Cyber-physical systems (CPSs) are important whenever computer technology interfaces with the physical world as it does in self-driving cars or aircraft control support systems. Due to their many subtleties, controllers for cyber-physical systems deserve to be held to the highest correctness standards. Their correct functioning is crucial, which explains the broad interest in safety analysis technology for their mathematical models, which are called hybrid systems because they combine discrete dynamics with continuous dynamics. Differential dynamic logic (dL) provides logical specification and rigorous reasoning techniques for hybrid systems. The logic dL is implemented in the theorem prover KeYmaera X, which has been instrumental in verifying ground robot controllers, railway systems, and the next-generation airborne collision avoidance system ACAS X. This chapter provides an informal overview of this logical approach to CPS safety that is detailed in a recent textbook on Logical Foundations of Cyber-Physical Systems. It also explains how safety guarantees obtained in the land of verified models reach the level of CPS execution unharmed.
Progressive Domain Adaptation for Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly, yet supervised models do not generalize well when testing on images from a different distribution. Domain adaptation provides a solution by adapting existing labels to the target testing data. However, a large gap between domains could make adaptation a challenging task, which leads to unstable training processes and sub-optimal results. In this paper, we propose to bridge the domain gap with an intermediate domain and progressively solve easier adaptation subtasks. This intermediate domain is constructed by translating the source images to mimic the ones in the target domain. To tackle the domain-shift problem, we adopt adversarial learning to align distributions at the feature level. In addition, a weighted task loss is applied to deal with unbalanced image quality in the intermediate domain. Experimental results show that our method performs favorably against the state-of-the-art method in terms of the performance on the target domain.
Learning eating environments through scene clustering
Venue: arXiv cs.CV 2019
Year: 2019

It is well known that dietary habits have a significant influence on health. While many studies have been conducted to understand this relationship, little is known about the relationship between eating environments and health. Yet researchers and health agencies around the world have recognized the eating environment as a promising context for improving diet and health. In this paper, we propose an image clustering method to automatically extract the eating environments from eating occasion images captured during a community dwelling dietary study. Specifically, we are interested in learning how many different environments an individual consumes food in. Our method clusters images by extracting features at both global and local scales using a deep neural network. The variation in the number of clusters and images captured by different individual makes this a very challenging problem. Experimental results show that our method performs significantly better compared to several existing clustering approaches.
Comparison of Quality Indicators in User-generated Content Using Social Media and Scholarly Text
Venue: arXiv cs.CL 2019
Year: 2019

Predicting the quality of a text document is a critical task when presented with the problem of measuring the performance of a document before its release. In this work, we evaluate various features including those extracted from the text content (textual) and those describing higher-level characteristics of the text (meta) features that are not directly available from the text, and show how these features inform prediction of document quality in different ways. Moreover, we also compare our methods on both social user-generated data such as tweets, and scholarly user-generated data such as academic articles, showing how the same features differently influence prediction of quality across these disparate domains.
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
Venue: arXiv cs.CL 2019
Year: 2019

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion. Traditionally, these aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question of whether, and to what extent, contextualized representations can be used to improve DPP modeling. Our findings suggest that, despite the success of deep representations, it remains necessary to combine them with surface indicators for effective identification of summary sentences.
Fairness Sample Complexity and the Case for Human Intervention
Venue: arXiv cs.LG 2019
Year: 2019

With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accuracy, when subdivided by specific sensitive variable subgroups. The reasons for these discrepancies are varied and not limited to the influence of mitigating variables, institutional bias, underlying population distributions as well as sampling bias. Among the numerous definitions of fairness that exist, we argue that at a minimum, principled ML practices should ensure that classification predictions are able to mirror the underlying sub-population distributions. However, as the number of sensitive variables increase, populations meeting at the intersectionality of these variables may simply not exist or may not be large enough to provide accurate samples for classification. In these increasingly likely scenarios, we make the case for human intervention and applying situational and individual definitions of fairness. In this paper we present lower bounds of subgroup sample complexity for metric-fair learning based on the theory of Probably Approximately Metric Fair Learning. We demonstrate that for a classifier to approach a definition of fairness in terms of specific sensitive variables, adequate subgroup population samples need to exist and the model dimensionality has to be aligned with subgroup population distributions. In cases where this is not feasible, we propose an approach using individual fairness definitions for achieving alignment. We look at two commonly explored UCI datasets under this lens and suggest human interventions for data collection for specific subgroups to achieve approximate individual fairness for linear hypotheses.
Double-Sparsity Learning Based Channel-and-Signal Estimation in Massive MIMO with Generalized Spatial Modulation
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known channel or known signal). In the BCSE algorithm, short pilots are employed to remove the phase and permutation ambiguities after sparse matrix factorization. To utilize the short pilots more efficiently, we further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm to incorporate the estimation of the phase and permutation ambiguities into the iterative message-passing process. We show that the SBCSE algorithm substantially outperforms the counterpart algorithms including the BCSE algorithm in the short-pilot regime.
Assessing the Adoption of Virtual Learning Environments in Primary Schools: An Activity Oriented Study of Teacher's Acceptance
Venue: arXiv cs.CY 2019
Year: 2019

This article studies the conditions of use of a VLE (Virtual Learning Environment) by primary school teachers. It first presents a triangulated model to explore Virtual Learning Environments' adoption in primary schools. The theoretical models cover three approaches: the social acceptance, the practical acceptance and the situated acceptance. The situated acceptance of teachers is studied according to the model by using activity theory and qualitative methods (individual and collective interviews). Our study describes how teachers (8 participants) perceived the role of the VLE in the evolution of their working practices (maintaining, transforming or restricting existent practices), in their relationship with parents and in the follow-up of their students.
Mixing realities for sketch retrieval in Virtual Reality
Venue: arXiv cs.HC 2019
Year: 2019

Drawing tools for Virtual Reality (VR) enable users to model 3D designs from within the virtual environment itself. These tools employ sketching and sculpting techniques known from desktop-based interfaces and apply them to hand-based controller interaction. While these techniques allow for mid-air sketching of basic shapes, it remains difficult for users to create detailed and comprehensive 3D models. In our work, we focus on supporting the user in designing the virtual environment around them by enhancing sketch-based interfaces with a supporting system for interactive model retrieval. Through sketching, an immersed user can query a database containing detailed 3D models and replace them into the virtual environment. To understand supportive sketching within a virtual environment, we compare different methods of sketch interaction, i.e., 3D mid-air sketching, 2D sketching on a virtual tablet, 2D sketching on a fixed virtual whiteboard, and 2D sketching on a real tablet. %using a 2D physical tablet, a 2D virtual tablet, a 2D virtual whiteboard, and 3D mid-air sketching. Our results show that 3D mid-air sketching is considered to be a more intuitive method to search a collection of models while the addition of physical devices creates confusion due to the complications of their inclusion within a virtual environment. While we pose our work as a retrieval problem for 3D models of chairs, our results can be extrapolated to other sketching tasks for virtual environments.
Hierarchical Prototype Learning for Zero-Shot Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Zero-Shot Learning (ZSL) has received extensive attention and successes in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. Key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary semantic prototypes (e.g., word or attribute vectors). However, the popularly learned projection functions in previous works cannot generalize well due to non-visual components included in semantic prototypes. Besides, the incompleteness of provided prototypes and captured images has less been considered by the state-of-the-art approaches in ZSL. In this paper, we propose a hierarchical prototype learning formulation to provide a systematical solution (named HPL) for zero-shot recognition. Specifically, HPL is able to obtain discriminability on both seen and unseen class domains by learning visual prototypes respectively under the transductive setting. To narrow the gap of two domains, we further learn the interpretable super-prototypes in both visual and semantic spaces. Meanwhile, the two spaces are further bridged by maximizing their structural consistency. This not only facilitates the representativeness of visual prototypes, but also alleviates the loss of information of semantic prototypes. An extensive group of experiments are then carefully designed and presented, demonstrating that HPL obtains remarkably more favorable efficiency and effectiveness, over currently available alternatives under various settings.
Coalitional Games with Stochastic Characteristic Functions and Private Types
Venue: arXiv cs.GT 2019
Year: 2019

The research on coalitional games has focused on how to share the reward among a coalition such that players are incentivised to collaborate together. It assumes that the (deterministic or stochastic) characteristic function is known in advance. This paper studies a new setting (a task allocation problem) where the characteristic function is not known and it is controlled by some private information from the players. Hence, the challenge here is twofold: (i) incentivize players to reveal their private information truthfully, (ii) incentivize them to collaborate together. We show that existing reward distribution mechanisms or auctions cannot solve the challenge. Hence, we propose the very first mechanism for the problem from the perspective of both mechanism design and coalitional games.
Rhoban Football Club: RoboCup Humanoid KidSize 2019 Champion Team Paper
Venue: arXiv cs.RO 2019
Year: 2019

In 2019, Rhoban Football Club reached the first place of the KidSize soccer competition for the fourth time and performed the first in-game throw-in in the history of the Humanoid league. Building on our existing code-base, we improved some specific functionalities, introduced new behaviors and experimented with original methods for labeling videos. This paper presents and reviews our latest changes to both software and hardware, highlighting the lessons learned during RoboCup.
A Curious Link Between Prime Numbers, the Maundy Cake Problem and Parallel Sorting
Venue: arXiv cs.DS 2019
Year: 2019

We present new theoretical algorithms that sums the n-ary comparators output in order to get the permutation indices in order to sort a sequence. By analysing the parallel ranking algorithm, we found that the special comparators number of elements it processes divide the number of elements to be sorted. Using the divide and conquer method, we can express the sorting problem into summing output of comparators taking a prime number of elements, given that this prime number divides the initial disordered sequence length. The number of sums is directly related to the Maundy cake problem. Furthermore, we provide a new sequence that counts the number of comparators used in the algorithms.
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
Venue: arXiv cs.CV 2019
Year: 2019

Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large variant on sizes and with occlusions remains a challenging problem. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and a concatenation layer which perform feature dimension squeezing, feature elements manipulation and convolutional features combination from multiple CNN layers, respectively. We proposed two different gate models which can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting those small-size and occluded pedestrians.
Convolution Inference via Synchronization of a Coupled CMOS Oscillator Array
Venue: arXiv cs.ET 2019
Year: 2019

Oscillator neural networks (ONN) are a promising hardware option for artificial intelligence. With an abundance of theoretical treatments of ONNs, few experimental implementations exist to date. In contrast to prior publications of only building block functionality, we report a practical experimental demonstration of neural computing using an ONN. The arrays contain 26 CMOS ring oscillators in the GHz range of frequencies tuned by image data and filters. Synchronization of oscillators results in an analog output voltage approximating convolution neural network operation.
A Coupled CMOS Oscillator Array for 8ns and 55pJ Inference in Convolutional Neural Networks
Venue: arXiv cs.ET 2019
Year: 2019

Oscillator neural networks (ONN) based on arrays of 26 CMOS ring oscillators designed and fabricated. ONN are used for inference of dot products with image fragments and kernels necessary for convolutional neural networks. The inputs are encoded as frequency shifts of oscillators using current DACs. Degree of match (DOM) is determined from oscillators synchronization. Measurements demonstrate high correlation of DOM and dot products. Inference requires the time of 8ns and energy of 55pJ.
Platoon trajectories generation: A unidirectional interconnected LSTM-based car following model
Venue: arXiv cs.LG 2019
Year: 2019

Car following models have been widely applied and made remarkable achievements in traffic engineering. However, the traffic micro-simulation accuracy of car following models in a platoon level, especially during traffic oscillations, still needs to be enhanced. Rather than using traditional individual car following models, we proposed a new trajectory generation approach to generate platoon level trajectories given the first leading vehicle's trajectory. In this paper, we discussed the temporal and spatial error propagation issue for the traditional approach by a car following block diagram representation. Based on the analysis, we pointed out that error comes from the training method and the model structure. In order to fix that, we adopt two improvements on the basis of the traditional LSTM based car following model. We utilized a scheduled sampling technique during the training process to solve the error propagation in the temporal dimension. Furthermore, we developed a unidirectional interconnected LSTM model structure to extract trajectories features from the perspective of the platoon. As indicated by the systematic empirical experiments, the proposed novel structure could efficiently reduce the temporal and spatial error propagation. Compared with the traditional LSTM based car following model, the proposed model has almost 40% less error. The findings will benefit the design and analysis of micro-simulation for platoon level car following models.
Tight Running Time Lower Bounds for Strong Inapproximability of Maximum $k$-Coverage, Unique Set Cover and Related Problems (via $t$-Wise Agreement Testing Theorem)
Venue: arXiv cs.CC 2019
Year: 2019

We show, assuming the (randomized) Gap Exponential Time Hypothesis (Gap-ETH), that the following tasks cannot be done in $T(k) \cdot N^{o(k)}$-time for any function $T$ where $N$ denote the input size: - $\left(1 - \frac{1}{e} + ε\right)$-approximation for Max $k$-Coverage for any $ε> 0$, - $\left(1 + \frac{2}{e} - ε\right)$-approximation for $k$-Median (in general metrics) for any constant $ε> 0$. - $\left(1 + \frac{8}{e} - ε\right)$-approximation for $k$-Mean (in general metrics) for any constant $ε> 0$. - Any constant factor approximation for $k$-Unique Set Cover, $k$-Nearest Codeword Problem and $k$-Closest Vector Problem. - $(1 + δ)$-approximation for $k$-Minimum Distance Problem and $k$-Shortest Vector Problem for some $δ> 0$. Since these problems can be trivially solved in $N^{O(k)}$ time, our running time lower bounds are essentially tight. In terms of approximation ratios, Max $k$-Coverage is well-known to admit polynomial-time $\left(1 - \frac{1}{e}\right)$-approximation algorithms, and, recently, it was shown that $k$-Median and $k$-Mean are approximable to within factors of $\left(1 + \frac{2}{e}\right)$ and $\left(1 + \frac{8}{e}\right)$ respectively in FPT time [Cohen-Addad et al. 2019]; hence, our inapproximability ratios are also tight for these three problems. For the remaining problems, no non-trivial FPT approximation algorithms are known. The starting point of all our hardness results mentioned above is the Label Cover problem (with projection constraints). We show that Label Cover cannot be approximated to within any constant factor in $T(k) \cdot N^{o(k)}$ time, where $N$ and $k$ denote the size of the input and the number of nodes on the side with the larger alphabet respectively. With this hardness, the above results follow immediately from known reductions...
Age-upon-Decisions Minimizing Scheduling in Internet of Things: To be Random or to be Deterministic?
Venue: arXiv cs.IT 2019
Year: 2019

We consider an Internet of Things (IoT) system in which a sensor delivers updates to a monitor with exponential service time and first-come-first-served (FCFS) discipline. We investigate the freshness of the received updates and propose a new metric termed as \textit{Age upon Decisions (AuD)}, which is defined as the time elapsed from the generation of each update to the epoch it is used to make decisions (e.g., estimations, inferences, controls). Within this framework, we aim at improving the freshness of updates at decision epochs by scheduling the update arrival process and the decision making process. Theoretical results show that 1) when the decisions are made according to a Poisson process, the average AuD is independent of decision rate and would be minimized if the arrival process is periodic (i.e., deterministic); 2) when both the decision process and the arrive process are periodic, the average AuD is larger than, but decreases with decision rate to, the average AuD of the corresponding system with Poisson decisions (i.e., random); 3) when both the decision process and the arrive process are periodic, the average AuD can be further decreased by optimally controlling the offset between the two processes. For practical IoT systems, therefore, it is suggested to employ periodic arrival processes and random decision processes. Nevertheless, making periodical updates and decisions with properly controlled offset also is a promising solution if the timing information of the two processes can be accessed by the monitor.